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George E. Dahl
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G Hinton, L Deng, D Yu, GE Dahl, A Mohamed, N Jaitly, A Senior, ...
IEEE Signal processing magazine 29 (6), 82-97, 2012
140202012
Neural message passing for quantum chemistry
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
International conference on machine learning, 1263-1272, 2017
92402017
On the importance of initialization and momentum in deep learning
I Sutskever, J Martens, G Dahl, G Hinton
International conference on machine learning, 1139-1147, 2013
65942013
Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition
G Dahl, D Yu, L Deng, A Acero
Audio, Speech, and Language Processing, IEEE Transactions on, 1-1, 2010
40452010
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
39442018
Acoustic modeling using deep belief networks
A Mohamed, GE Dahl, G Hinton
IEEE transactions on audio, speech, and language processing 20 (1), 14-22, 2011
22752011
Deep convolutional neural networks for large-scale speech tasks
TN Sainath, B Kingsbury, G Saon, H Soltau, A Mohamed, G Dahl, ...
Neural networks 64, 39-48, 2015
20412015
Improving deep neural networks for LVCSR using rectified linear units and dropout
GE Dahl, TN Sainath, GE Hinton
2013 IEEE international conference on acoustics, speech and signal …, 2013
19222013
Deep neural nets as a method for quantitative structure–activity relationships
J Ma, RP Sheridan, A Liaw, GE Dahl, V Svetnik
Journal of chemical information and modeling 55 (2), 263-274, 2015
13712015
Detecting cancer metastases on gigapixel pathology images
Y Liu, K Gadepalli, M Norouzi, GE Dahl, T Kohlberger, A Boyko, ...
arXiv preprint arXiv:1703.02442, 2017
7912017
Deep belief networks for phone recognition
A Mohamed, G Dahl, G Hinton
NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009
6762009
Prediction errors of molecular machine learning models lower than hybrid DFT error
FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ...
Journal of chemical theory and computation 13 (11), 5255-5264, 2017
6592017
Large-scale malware classification using random projections and neural networks
GE Dahl, JW Stokes, L Deng, D Yu
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
6542013
Large scale distributed neural network training through online distillation
R Anil, G Pereyra, A Passos, R Ormandi, GE Dahl, GE Hinton
arXiv preprint arXiv:1804.03235, 2018
5152018
Measuring the effects of data parallelism on neural network training
CJ Shallue, J Lee, J Antognini, J Sohl-Dickstein, R Frostig, GE Dahl
Journal of Machine Learning Research 20 (112), 1-49, 2019
4532019
Phone recognition with the mean-covariance restricted Boltzmann machine
G Dahl, MA Ranzato, A Mohamed, GE Hinton
Advances in neural information processing systems 23, 2010
4472010
Deep belief networks using discriminative features for phone recognition
A Mohamed, TN Sainath, G Dahl, B Ramabhadran, GE Hinton, ...
2011 IEEE international conference on acoustics, speech and signal …, 2011
4092011
Multi-task neural networks for QSAR predictions
GE Dahl, N Jaitly, R Salakhutdinov
arXiv preprint arXiv:1406.1231, 2014
4052014
On empirical comparisons of optimizers for deep learning
D Choi
arXiv preprint arXiv:1910.05446, 2019
3932019
Artificial intelligence–based breast cancer nodal metastasis detection: insights into the black box for pathologists
Y Liu, T Kohlberger, M Norouzi, GE Dahl, JL Smith, A Mohtashamian, ...
Archives of pathology & laboratory medicine 143 (7), 859-868, 2019
3882019
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